Techniques to add smart device information to machine learning for increased context

ABSTRACT

Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.

BACKGROUND

Artificial intelligence has enabled computer systems to conductconversations, either textual or computer-generated speech, with humans.Most people encounter these conversant computer systems in the form ofcustomer service chatbots. These customer service oriented chatbots mayuse leading questions to elicit an appropriate response from the humanparticipant. As a result, the conversation while straight forward has a“mechanical” quality to it that may be off-putting to some of the humanparticipants.

While canned expressions, such as “I hope you are having a good day” orfor repeat callers, “Thank you contacting us again,” may provide a morerealistic feel to the conversation, these canned expressions may onlymake the conversation flow seem forced and disingenuous since there isno context in the conversation for such canned expressions. Accordingly,a problem with chatbot training is that the chatbot models often defaultto the most generic speech patterns due to a lack of context obtainedfrom the training data.

There is a need for ways to mitigate the mechanical, or “machine-like”speech quality of current chatbot conversations and provide morerealistic conversational flow and context by referencing real-lifeaspects of a human participant.

SUMMARY

Disclosed is an example of an apparatus including a memory andprocessing circuitry. The memory may store programming code. Theprocessing circuitry may be coupled to the memory and have an input andan output. The processing circuitry is operable to execute the storedprogramming code that causes the processing circuitry to receive firstdata from a first smart device. A data type of the received first datamay be determined. The determined data type may indicate a category ofthe first smart device. Based on the determined data type, the receivedfirst data may be transformed into transformed data using a datatransformation process that receives an input from a machine learningalgorithm. The transformed data may be output to a chatbot training datastructure, which is coupled via the output to the processing circuitry.

A system example is also disclosed. The system may include a chatbotdata warehouse, a communication interface, a memory, and a server. Thecommunication interface may be coupled to the chatbot data warehouse andto an external network. The communication interface receives, via theexternal network, anonymized non-dialog data from smart devices. Theserver is coupled to the memory and the communication interface. Theserver includes processing circuitry that executes the programming codestored in the memory. The execution of the programming code causes theprocessing circuitry to obtain, via the communication interface, thenon-dialog data. A data type of the received non-dialog data isidentified. Based on the identified data type, at least one machinelearning-based data transformation from a plurality of machinelearning-based data transformations is selected. The non-dialog data isinput into each of the at least one selected machine learning-based datatransformations. The transformed data may be received from each of theat least one selected machine learning-based data transformations intowhich the non-dialog data was input and stored in the chatbot datawarehouse. The transformed data is formatted in the same format as thedialog data stored in the chatbot data warehouse.

An example of non-transitory computer-readable storage medium storingcomputer-readable program code executable by a processor is alsodisclosed. The execution of the computer-readable program code causingthe processor to receive, via a coupling to an external network, a dataset containing non-dialog data, wherein the data set is generated basedon data provided by a smart device. The non-dialog data is extractedfrom the received data set. A data type of the extracted non-dialog datais determined. Based on the determined data type, a machinelearning-based data transformation to be applied to the extractednon-dialog data is selected. The selected machine learning-based datatransformation is applied to the extracted non-dialog data to obtainstandardized non-dialog data; and the standardized non-dialog data isstored in a chatbot training data structure.

Additional objects, advantages and novel features of the examples willbe set forth in part in the description which follows, and in part willbecome apparent to those skilled in the art upon examination of thefollowing and the accompanying drawings or may be learned by productionor operation of the examples. The objects and advantages of the presentsubject matter may be realized and attained by means of themethodologies, instrumentalities and combinations particularly pointedout in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a data collation and chatbot trainingsystem.

FIG. 2 is an overview of data input, transformation, and usage with anexample of a system, such as that shown in FIG. 1.

FIG. 3 is an example of a process flow usable in the system example ofFIG. 2.

FIG. 4A illustrates an example of a data transformation subprocess flowthat processes the smart device data into a standardized data typeusable with machine learning systems.

FIG. 4B illustrates hardware components and data flow related to thedata transformation process example of FIG. 4A.

FIG. 5 illustrates an embodiment of an exemplary computing architecturesuitable for implementing the devices and systems described with respectto FIGS. 1-4B.

FIG. 6 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects of thedisclosed subject matter, such as those shown in and described withreference to FIGS. 1-4B.

DETAILED DESCRIPTION

Various embodiments are generally directed to techniques to perform ageneralization of data from non-dialog data sources, such as a wearablefitness smart device, a wearable smart device, an exercise device, apersonal assistance device; audio system assistance device, or a homeautomation device, or the like. Embodiments include an apparatus havinga memory storing programming code and processing circuitry coupled tothe memory and a chatbot training data structure. The processingcircuitry may have an input and an output and is operable to execute thestored programming code that causes the processing circuitry to performfunctions. In an example, the functions may include receiving first datafrom a first smart device and determining a data type of the receivedfirst data. Based on the determined data type, the processing circuitrytransforms the received first data using an input from a machinelearning algorithm into transformed data. The transformed data is outputby the processing circuitry to a chatbot training data structure.

Reference is now made to the drawings, wherein like reference numeralsare used to refer to like elements throughout. In the followingdescription, for purpose of explanation, numerous specific details areset forth in order to provide a thorough understanding thereof. It maybe evident, however, that the novel embodiments can be practiced withoutthese specific details. In other instances, well-known structures anddevices are shown in block diagram form to facilitate a descriptionthereof. The intention is to cover all modifications, equivalents, andalternatives within the scope of the claims.

The described examples address the problem of underutilization ofsmart-device data with respect to machine learning in an enterprisenetwork, such the banking space. Huge amounts of data from smart devicesare typically discarded or overlooked, and machine learning requiressignificant amounts of data to be useful. The related figures and thediscussion describe a method, a computer readable medium, an apparatus,and a system that enable data from smart devices to be used to provide aconsistent way to gather information from users' smart devices,standardize the data, and incorporate that standardized data intoexisting and future machine learning techniques.

The described examples are substantially different from any“Internet-of-Things” or data mining techniques in general because theexamples enable a conversion of device data to data that is palatablefor machine learning algorithms. Existing methods that do involve a datapipeline from smart devices feature a disjoint set of technologies thatdo not enable a data transport. The example system, by coupling smartdevices and machine learning algorithms together, may be substantiallyseamless and allows for the learned orchestration of disparate datatypes together as well.

The described examples may obtain data from an enterprise customer whomay generate a huge amount of data on a daily basis that is currentlyunused. The unused data may take the form of physical activityrepresentations, textual conversations with other people, informationregarding listening or viewing habits and the content that is listenedto or viewed, wake up time, sleep time, exercise time or work time, andthe like. A benefit of the described examples is to serve the disparatedata, the data's relative context or a combination of both to chatbotmachine learning models as additional information and attributes fromwhich conversational context may be learned. An improvement provided bythe described examples is the surpassing of existing implementations ofInternet of Things solutions because the described examples provide astandardized method to collect data tailored to chatbots whilepreserving privacy. Data is preserved because raw personal data stayswith the user and is not obtained by the described enterprise.

A basic example of a data structure is to encapsulate smart device datainto matrices and vectors the definitions of which rely on a currententerprise system-owned machine learning model that is training in theenterprise network. These matrices and vectors provide data usable ascontextual and training data to chatbot machine learning algorithms inthe cloud or stored in traditional enterprise storage solutions. Thisinvolves a web of users' smart devices, the procurement of data fromthese smart devices, and the conversion of this raw data into astandardized data type.

An example of a practical application of this smart device datacollation is chatbot data collection. Not only may dialog between usersbe collected, but additional user non-dialog data may also be collectedas well. For example, inferences as to whether a user is excited orrelaxed may be drawn from non-dialog data, such as accelerometer datacollected from a smart phone, or whether a user is doubtful, stressed orexperiencing another emotion may be drawn from a pitch tone extractedfrom audio data of a conversation. The quantity of data that a usergenerates with all their smart devices is immense, and some trainingvalue ought to be extracted from it to avoid wasting an opportunity toobtain contextual data. Examples of non-dialog data includeaccelerometer data, sound-related data, such as pitch, timbre, intensityand duration, from dialog data, weather related data, or the like.Consider, for example, one type of data that most enterprises and othercompanies that train chatbots typically discard is health information.In the example of health information, many smart devices log basichealth data, such as number of steps walked per day, heart rate, numberof glasses of water drank, number of exercises (e.g., pushups, pullups,miles run, and the like), and so on. This health information may be usedas a metric that serves as an indicator of health, mood, and lifecircumstance. Without the presently described subject matter, this datawould continue to be discarded, or if naively collected, a complicateddata transformation step would be required. However, treating healthdata the same as other data by having a standard, learned transformationstep may allow a data scientist to gracefully add the transformed dataof the health information to their machine-learning model.

FIG. 1 illustrates an example of data collation and chatbot trainingsystem suitable for performing the described process examples. The datacollation and chatbot training system 100 may be part of an enterprisenetwork that is configured to receive data and collate the data andplace the data in a standardized format for use in training chatbots.Embodiments are not limited in this manner.

In embodiments, the data collation and chatbot training system 100 mayinclude data source(s) 102, a data transformation services system 104,and chatbot services system 106. These devices and systems may becoupled via one or more networking links 110, 120 and 130. For example,each of the data source(s) 102, the data transformation system 104, andthe chatbot system 106 may be coupled with network 105, e.g., anenterprise network, via one or more wired and/or wireless network links.In some embodiments, the data source(s) 102, the data transformationsystem 104, and the chatbot system 106 may be coupled via dedicatednetworking links 110, 120 and 130, e.g., a point-to-point wireless orwired network, a point-to-point fiber network, telephone network(dial-up modem network), or the like. Embodiments are not limited inthis manner.

In embodiments, the data collation and chatbot training system 100 maycollect and process data associated with data source(s) 102. Forexample, the system 100 may receive data from one or more different datasource(s) 102 either directly or via other connections. Examples of datasource(s) 102 may include computers, such as an enterprise-relatedapplication executing on the computer, a smart phones or tablets, bookreaders, such as a Kindle, wearable fitness smart devices, such as aFitbit®, or a Garmin® watch; a wearable smart device, such as an Apple®watch or Samsung® Gear®; an exercise device, such as a Peloton® bike ortreadmill; a personal assistance device, such as Google Home® or Amazon®Echo; an audio system assistance device, such as Amazon® Echo or Alexa®that connects, for example, to Amazon Prime® or Pandora music services,a home automation device (e.g., a door lock system, a security system, adoorbell camera, or the like), such as a number of Nest® devices, awebsite or cloud-based data storage location. Each of different datasources 102 may provide data having a data type specific to the datasource. In an example, a data source 102, such as a smart device, maycommunicate with a computer or other computing device executing theenterprise-related application, and upload data or information generatedby the data source, such as non-dialog data or information, to thecomputer or other computing device (shown in other examples). Eachcomputer may execute a respective instance of the multiple instances ofan enterprise-related application, which configures a respectiveprocessor of the computer to obtain the uploaded non-dialog data fordata transformation. The respective instance of the enterprise-relatedapplication may anonymize the non-dialog data and forward via anexternal network, such as the Internet or a cellular network, theanonymized non-dialog data to a server coupled to a communicationinterface (described in more detail with another example) for datatransformation. In a specific smart fitness device example, a Fitbitdevice may provide fitness-related data having a specific data type foruse with Fitbit-compatible computer/mobile applications or websites. TheFitbit-compatible computer/mobile applications or websites may be“universal” types of computer/mobile applications and websites capableof processing data provided by not only Fitbit devices, but any type ofsmart fitness device.

The enterprise-related application executing on the computer, othercomputing device may process the non-dialog data to determine a datatype of the data. The data type of the data may indicate the category ofthe smart device providing the data. For example, wearable smart fitnessdevices may provide non-dialog data in multiple different types havingdifferent formats. The data types provided by the respective datasources may be, for example, training center extensible markup language(XML) (TCX), Microsoft Excel (XLS), flexible and interoperable datatransfer protocol (FIT), comma-separated value (CSV), GPS exchangeformat (GPX) or the like; and the data included, may differ from onedata type to another. Therefore, a data type such as FIT may indicate afitness device as the category of the device providing the data, whileGPX may indicate a GPS device as the category of the device providingthe data. In addition, some data types may include additional datarelated to body, food, activities, and sleep patterns. The body dataprovided by a respective data type may vary between providing and notproviding information such as, for example, blood oxygen level, weight,heart rate, body mass index, calculated percentage of body fat, or thelike. The different types may also provide different levels of fitnessinformation, such as distance calculations as opposed to just positionalinformation (e.g. latitude and longitude coordinates). For example, inthe case of a global positioning system (GPS)-enabled fitness device,the fitness data, such as steps, heart rate and distance traveled,generated by the fitness device may be in a first format such as thoselisted above, while the GPS data may be in a second, different datatype, such as GPX. Alternatively, all data generated by the fitnessdevice including the GPS data may be stored in a single data type, suchas TCX or GPX. Other examples of non-dialog data obtained from differentdata sources include smart phone application preference settings, musicplaylists selected on audio system devices or music services, lists ofproducts ordered on-line, time of exit/entry in a home security system,and the like. All the different data may be used to develop context foruse in improving and further developing conversational context of achatbot.

Each of the different data types TCX, XLS, FIT, CSV, GPX or the like,may be transmitted in a form that includes data identifiers in the dataheader, or may have a format indicative of the respective format. Forexample, a TCX file may have “<Activities>” as an initial XML code incontrast to a code entry of a GPX file. Of course, to preserve userprivacy and prevent misuse of data, users would be able to withhold dataas well as having options to opt-out or opt-in to the data collectionprogram. It is important that users are in control of their data, and tokeep previously unused data unused.

The communication interface 290 may receive via the external networkanonymized non-dialog data from smart devices 221-223 that is forwardedto a server in the data transformation system 204.

In general, the described data transformation has an added benefit ofobscuring the content of the data itself; since specific information maybe converted into multidimensional matrices and vectors of numbers,identifying what specific values mean would be at least very difficult,if not impossible, and time-consuming. By informing users of the processand giving them a choice over their contribution, there is little chanceof deception or abuse.

The system 100 may also include the data transformation system 104. Thedata transformation system 104 may operate to transform the format ofdata by using a data transformation process/function/algorithm that isspecific to the data type of the data source 102. The datatransformation system 104 may be processing circuitry configured toexecute process programming code that causes the processing code toperform functions. Alternatively, the data transformation system 104 maybe implemented as a server coupled to a memory and the network 105. Thedata transformation system 104 structure and functions are described inmore detail with reference to the examples of FIGS. 2-6.

The chatbot system 106 may be coupled to the network 105 via the networkconnection 130. The chatbot system 106 may implement a group ofinteractive systems that provide services or information by interacting(e.g. “conversing”) with human users either via auditory (e.g.,telephones, audio equipped Internet devices) or textual systems, such astelephone text messaging, messaging clients built into websites. Thechatbot system 106 may include multiple different chatbots that are usedfor different types of interactions. For example, a first chatbot maycommunicate information about user financial services, while a secondchatbot may collect financial information for loan purposes.

Alternatively, some chatbots may be trained to converse in a regionaldialect of a particular geographical area. For example, a chatbot thathas trained on training data from data sources associated with theSouthern states of the United States may be selected to converse with ahuman user from Alabama, while a human user in Massachusetts may beconnected to a chatbot trained on data sources from Northern states ofthe United States.

The chatbot system 106 may, for example, include training and validationsystems that train and validate artificial intelligence or machinelearning algorithms (collectively referred to as “chatbot models”) usedby the interactive systems to converse (using machine-generated speechor text) with the human users. As used herein, the terms “converse” maymean exchanging information with a human user (or an intelligent devicecapable of interpreting audio or text content) via eithermachine-generated speech or by textual exchanges, and “speech” may beeither auditory or textual.

A technical justification for collecting this previously unused datafrom data sources, such as 102, follows from the importance ofuser-specific context during conversations to address the problem ofgeneric or mechanical speech (and textual) patterns from chatbots. Withthe significant amount of previously discarded data now incorporated ascontext input for machine learning models, chatbot models may betailored to users in ways previously not possible. For example, achatbot may not only have access to user-specific patterns of speech,but also information about habits, relationships, and motivations. Withadded context over a variety of fields of user interest, chatbot modelsmay attenuate or mitigate the use of mechanical speech (or text)patterns and provide speech (and text) generated according to new,attribute-centered techniques.

For example, the chatbot system 106 may operate almost continuously byreceiving new training data and inputting the new training data into theartificial intelligence or machine learning algorithms to improve thehuman user's experience with the respective chatbot and to improveperformance of a process guided by the chatbot by making the spoken ortextual conversation less formalistic. This new training data istransformed into suitable form for input to the chatbot models orpre-processing functions.

It may be helpful at this time discuss a more detailed example of thedata collation and transformation performed by system 100 with referenceto the more detailed example shown in FIG. 2.

FIG. 2 is an overview of data input, transformation, and usage with anexample of a system, such as that shown in FIG. 1. The system 200 mayinclude smart devices 221, 222, and 223, a data transformation apparatus204, a chatbot system 206, and a chatbot data warehouse 208.

The smart devices 221, 222, and 223 may be coupled to an externalnetwork (not shown in this example) that couples via communicationinterface 290 to the data transformation apparatus 204. Examples of thesmart devices 221, 222 and 223 may be similar to the data sources 102described with respect to FIG. 1. The communication interface 290 mayinclude couplings to networks, such as 105 of FIG. 1, the datatransformation system 204, the chatbot system 206, the chatbot datawarehouse 208, and other networks, such as an external network, andsystems (not shown). The communication interface 290 may receive via theexternal network anonymized non-dialog data from smart devices 221-223that is forwarded to a server in the data transformation system 204.

The chatbot data warehouse 208 may be a chatbot training data structurestoring data including transformed non-dialog data and dialog data. Thestored transformed non-dialog data and dialog data may be stored invarious formats including generalized formats for use by multipledifferent chatbot models.

The data transformation system 204 may comprise a data transformationapparatus 241 having processing circuitry 261 that executes programmingcode stored in a memory 269 coupled to the processing circuitry 261. Thedata transformation apparatus 241 may be implemented in the form of aserver or other computing device. The processing circuitry 261 mayinclude couplings to the communication interface 290 and to severalmachine learning-based data transformations, such as 242, 244, 246 and248. The machine learning-based data transformations may include avectorization transformation 242, word representation transformations244, such as the GloVe model, learned representations transformation246, and statistics-based transformation 248. Of course, the number ofmachine learning-based data transformations may include othertransformations, but for ease of discussion, only a few are listed.Other transformations may include matrix factorization, statisticalanalysis, semantic hashing, latent semantic indexing or the like. Themachine learning-based data transformations may receive input frommachine learning algorithms, such a regression algorithm, decision treealgorithms, support vector machines, k-nearest neighbors algorithms,K-means clustering algorithms, deep neural networks, dimensionalityreduction algorithms, or the like to improve the accuracy and efficiencyof the data transformation.

The chatbot system 206 may comprise several pre-processing modules orfunctions, such as hidden states processing module 211, the chatbotmodel initialization processing module 212, functions performed bypretrained chatbot models 213, and conditional information andattributes module 214, that pre-process data for input into one or moreof the chatbot models 216A-216D. The chatbot system 206 may also includeseparate training, validation and test data storage 217 that stores dataused to further train, test and validate the performance of the chatbotmodels. Client computing devices 109A and 109B may be coupled to thecommunication interface 290 and the data warehouse 208. The clientdevices 109A and 109B may retrieve via the communication interface 290transformed dialog data and non-dialog data from the chatbot datawarehouse 208 for use in training a chatbot.

For example, the transformed non-dialog data and dialog data stored inthe chatbot data warehouse 208 may be served or retrieved in variousformats to various enterprise chatbot models or pre-processingfunctions. A chatbot model may be an algorithm or function that operateson data supplied by pre-processing modules. The storage of the data mayhappen in the form of direct warehousing alongside the training datachatbots are already using for long-term use. This allows for non-dialogand dialog data collection from data sources, such as 221-223 and othersources (e.g., recorded customer service calls or the like), to keephappening regardless of whether a model is training. For time-dependentapplications, a particular chatbot model (e.g., 216A of chatbot models216A-216D) may be both training and be used to process a “liveconversation” with a user, data may be directly served to chatbot modelsand stored in-memory for training algorithms as hidden state vectorswhich do not disclose personal data as they are sub-products (i.e.,transformations) of the trainable parameters of a particular machinelearning model of the chatbot models 216A-D in chatbot system 206.

The processing circuitry 261 upon execution of the programming codestored in the memory may perform a few functions. Examples of thefunctions are described in more detail below with reference to anexample process shown in FIG. 3

FIG. 3 is an example of a process flow usable in the system example ofFIG. 2. The process 300 may be implemented by execution ofcomputer-readable program code stored on a non-transitorycomputer-readable storage medium by processing circuitry such as thatdescribed above with reference to the example illustrated in FIG. 2 andwith respect to later examples.

In the process 300, processing circuitry such as 261 of FIG. 2 mayreceive, via a coupling, such as communication interface 290, to anexternal network, a data set containing non-dialog data (305). The dataset may be generated based on data provided by a smart device. Forexample, the received data may be received from a first smart devicesuch as a wearable fitness device 222 of FIG. 2, and, as such, may bereferred to as first data.

The received data set may include dialog data, non-dialog data and otherdata that is not relevant to the chatbot training. The processingcircuitry may extract the non-dialog data from the received data set(310). The processing circuitry may determine a data type (e.g., GPX,FIT, TCX, CSV or the like) of the extracted non-dialog data. In keepingwith the example of receiving data from a first smart device, the datatype of the received first data, may be determined by parsing the firstdata, and comparing the parsed first data to data identifiers oridentified data patterns stored in a data structure, such as memory 269.Based on results of the comparison, the processing circuitry mayidentify the data type of the received first data. For example, thedetermined data type may be TCX. In some examples, the processingcircuitry may output the identified data type as the determined datatype for use by a selected transformation process when transforming thereceived first data into transformed data.

At 320, the process 300 selects based on the determined data type, amachine learning-based data transformation process from several datatransformation processes to be applied to the extracted non-dialog data.For example, certain data transformation processes may be better suitedtoward processing TCX formatted data than FIT formatted data, or viceversa. Also, the different data types provide different levels offitness information, such as distance calculations as opposed to justpositional information (e.g., latitude and longitude coordinates). In anexample, the selected data transformation process may use the identifieddata type during the transformation of the data.

The processing circuitry may apply the selected machine learning-baseddata transformation to the extracted non-dialog data to obtainstandardized non-dialog data (325). A purpose of the data transformationstep is to convert the data to a format usable for machine learning. Forexample, the processing circuitry may input the obtained non-dialoginformation into the selected data transformation process. The inputattributes sent to the selected transformation process may be weightedbased on outputs from a machine learning algorithm trained usingpreviously obtained non-dialog information of a same data type as thedetermined data type. As such, the data transformation processes arealso evolving based on machine-learning that uses historical data outputfrom the selected data transformations. The selected data transformationprocess may generate data points and data point probability informationas the transformed data. The transformed data may be output by theprocessing circuitry to a chatbot training data structure for storage(330). The chatbot training data structure may be located, for example,in the data warehouse 208 of FIG. 2, and may store the transformednon-dialog data in a chatbot training data structure. The transformednon-dialog data may be standardized in format for use by a chatbotsystem, such as 106 of FIG. 1 or 206 of FIG. 2.

The process 300 may occur contemporaneously for multiple smart devices.In a contemporaneous process, the processing circuitry may receive firstdata from a first smart device such as smart fitness device 222 of FIG.2, and receive second data from a second smart device, such as a homemusic device, such as 223 of FIG. 2. In the contemporaneous process, theprocessing circuitry may perform steps 305 to 330 on the received seconddata as explained above with respect to the first data. Depending uponthe determined data type of the second data, another data transformationprocess may be selected to transform the received second data intotransformed second data. The other data transformation process mayreceive input from the same machine learning algorithm that providedinput to the data transformation process selected to process the firstdata or may receive inputs from other machine learning algorithms. Afterthe second data is transformed by the other data transformation, thetransformed second data is output to the chatbot training datastructure.

FIG. 4A illustrates an example of a data transformation subprocess thatobtains smart device data and processes the smart device data into astandardized data type usable with machine learning systems. Asmentioned above, data transformation processes may includevectorization, word representation, learned representationstransformation 246. The data transformation process may be viewed as asubprocess flow, such as process step 325 of FIG. 3. For example, thedata transformation process may obtain smart device data and processesthe smart device data into a standardized data type usable with machinelearning systems provided by the chatbot systems 106 of FIG. 1 and 206of FIG. 2.

FIG. 4B illustrates hardware components and data flow related to thedata transformation process example of FIG. 4A. The example datatransformation process of FIG. 4A is explained with reference tohardware component examples shown in FIG. 4B.

The data transformation process may itself be a machine learning model.Applying a machine learning model to the data transformation has befound to be effective for the collation of disparate data (i.e. the datafrom the smart devices) into standardized representations. In someexamples, the machine learning-based data transformation processes mayreceive feedback from the chatbot models, such as 216A-D of FIG. 2,operating downstream in a chatbot system, such as 206 of FIG. 2. Thesystem parts that are relevant to the discussion of FIG. 4A are a datatransformation apparatus 410 and a selected data transformation process416. The data transformation apparatus 410 provides the output of thedata transformation process 416 to a pre-processor computer 431 and achatbot model 435, which are components of a chatbot system such as 206of FIG. 2. The data transformation apparatus 410 may include processingcircuitry 412 and memory 414.

In one aspect, the data transformation is a way for models downstream torequest data and be served that data without a data scientist having toaccount for its format and source. An example is a chatbot trainingmodel may continuously poll for a relationship graph between the user itis learning and the user's friends. Of course, the user's friend may bepreviously identified and, via an opt-in/opt-out process, has permittedthe collection of information related to the relationship between thefriend and the user.

Like previously described examples, non-dialog information from a user'ssmart fitness device 405 may be received by the data transformationapparatus 410. The processing circuitry 412 by executing programmingcode stored in memory 414 may determine a correspondence between certaindata elements in the obtained non-dialog information with a list ofhistorical data elements. The correspondence determination may be madeby applying a probability function to data elements in the obtainednon-dialog information. When applying the selected machinelearning-based data transformation to the extracted non-dialog data, theprocessing circuitry 412 may utilize a process 440 illustrated in FIG.4B.

For example, the processing circuitry 412 may determine whether the datain the non-dialog information includes a data identifier correspondingto either a wearable fitness smart device, a wearable smart device, anexercise device, a personal assistance device, an audio systemassistance device, a home automation device or the like. Upondetermining that the non-dialog information data includes a dataidentifier, the data identifier may be extracted or copied, and matchedto at least one of the data identifiers stored in a data structurecontaining data identifiers either in the memory 412 or a chatbot datawarehouse, such as 208 of FIG. 2.

In more detail, a matching data identifier may be extracted as theresult of the comparison, the processing circuitry 412 executing process440 may, for example, at 443, weight a data type attribute based on anoutput from a machine learning algorithm trained using previouslyobtained non-dialog information has data of the same data type as thedetermined data type. The weighting of the data type attribute may bedetermined for the selected data transformation process using a machinelearning algorithm trained using historical data of the determined datatype.

At 445, the processing circuitry 412 may input the weighted data typeattributes related to the determined data type of the extractednon-dialog data and the extracted non-dialog data into the selectedmachine learning-based data transformation. The processing circuitry 412may process at 447 the inputted weighted data type attributes related tothe determined data type of the extracted non-dialog data and theextracted non-dialog data.

As further explanation, the processing at 447 of the inputted weighteddata type attributes by the processing circuitry 412 may be performedaccording to the instructions of the selected data transformationprocess. For example, the selected data transformation process may use aprobability function. The inputted weighted data type attributes may beinputs into the probability function. The probability function appliedto data elements in the obtained or parsed non-dialog information usingthe inputted determined weighted input attributes to determine acorrespondence between certain data elements in the obtained or parsednon-dialog information with a list of historical data elements. Based onthe determined correspondence, the selected data transformation processmay be applied to the certain data elements determined to havecorrespondence.

After the processing in 447, data points, and, for each data point, datapoint probability information may be output at 449 as standardizednon-dialog data to a chatbot training data structure.

The standardized non-dialog data including the data points and the datapoint probability information for each data point may be generated as astandardized chain of values. The standardized chain of values may, forexample, include individual relative probability values indicating aprobability of certain data elements from the non-dialog data having thedetermined correspondence and respective relationship probability valuesrelated to a probability of respective data elements of the certain dataelements having a relationship. The standardized chain of values may bedifferent for each selected data transformation process. Thestandardized chain of values may be stored, for example, in the chatbotdata warehouse

The processing circuitry 412 may output the standardized chain of valuesto the pre-processor 431 in a chatbot system. The pre-processor 431 mayperform further operations to generate processed standardized values.For example, the pre-processor 431 may provide processing related tohidden states, data initialization for chatbot models, pre-training forchatbot models, conditional information and attributes and the like.

The processed standardized values may be sent to one chatbot model 435of many chatbot models, such as 216A-D of FIG. 2, where the processedstandardized values may be incorporated into a chatbot output based onthe processed standardized values. For example, the processedstandardized values may be incorporated into a timestep, where theleft-hand sign of the equation are the Long Short-Term Memory (LSTM)algorithm components, and the right-hand side of the equation is thevector representation of the output.

FIG. 5 illustrates an embodiment of an exemplary computing architecture500 suitable for implementing the devices and systems described withrespect to the various examples FIGS. 1-4B. In one embodiment, thecomputing architecture 500 may include a hardware or software componentor be implemented as part of system 100 or 200. For example, thecomputing architecture 500 may implement the data transformationapparatus 241, 410 or system 104 or 204, or chatbot system 106 or 206.

As used in this application, the terms “system” and “component” areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution, examples of which are provided by the exemplary computingarchitecture 500. For example, a component can be, but is not limited tobeing, a process running on a processor, a processor, a hard disk drive,multiple storage drives (of optical and/or magnetic storage medium), anobject, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution, and a component canbe localized on one computer and/or distributed between two or morecomputers. Further, components may be communicatively coupled to eachother by various types of communications media to coordinate operations.The coordination may involve the uni-directional or bi-directionalexchange of information

The computing architecture 500 includes various common computingelements, such as one or more processors, multi-core processors,co-processors, memory units, chipsets, controllers, peripherals,interfaces, oscillators, timing devices, video cards, audio cards,multimedia input/output (I/O) components, power supplies, and so forth.The embodiments, however, are not limited to implementation by thecomputing architecture 500.

As shown in FIG. 5, the computing architecture 500 includes a processingunit 504, a system memory 506 and a system bus 508. The processing unit504 can be any of various commercially available computer processingunits.

The system bus 508 provides an interface for system componentsincluding, but not limited to, the system memory 506 to the processingunit 504. The system bus 508 can be any of several types of busstructure that may further interconnect to a memory bus (with or withouta memory controller), a peripheral bus, and a local bus using any of avariety of commercially available bus architectures. Interface adaptersmay connect to the system bus 508 via slot architecture. Example slotarchitectures may include without limitation Accelerated Graphics Port(AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA),Micro Channel Architecture (MCA), NuBus, Peripheral ComponentInterconnect (Extended) (PCI(X)), PCI Express, Personal Computer MemoryCard International Association (PCMCIA), and the like.

The computing architecture 500 may include or implement various articlesof manufacture. An article of manufacture may include acomputer-readable storage medium to store logic. Examples of anon-transitory computer-readable storage medium may include any tangiblemedia capable of storing electronic data, including volatile memory ornon-volatile memory, removable or non-removable memory, erasable ornon-erasable memory, writeable or re-writeable memory, and the like.Examples of logic may include executable computer program instructionsimplemented using any suitable type of code, such as source code,compiled code, interpreted code, executable code, static code, dynamiccode, object-oriented code, visual code, and the like. Embodiments mayalso be at least partly implemented as instructions contained in or on anon-transitory computer-readable medium, which may be read and executedby one or more processors to enable performance of the operationsdescribed herein.

The system memory 506 may include various types of computer-readablestorage media in the form of one or more higher speed memory units, suchas read-only memory (ROM), random-access memory (RAM), dynamic RAM(DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), staticRAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, polymermemory such as ferroelectric polymer memory, ovonic memory, phase changeor ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS)memory, magnetic or optical cards, an array of devices such as RedundantArray of Independent Disks (RAID) drives, solid state memory devices(e.g., USB memory, solid state drives (SSD) and any other type ofstorage media suitable for storing information. In the illustratedembodiment shown in FIG. 5, the system memory 506 can includenon-volatile memory 510 and/or volatile memory 512. A basic input/outputsystem (BIOS) can be stored in the non-volatile memory 510.

The computer 502 may include various types of computer-readable storagemedia in the form of one or more lower speed memory units, including aninternal (or external) hard disk drive (HDD) 514, a magnetic floppy diskdrive (FDD) 516 to read from or write to a removable magnetic disk 518,and an optical disk drive 520 to read from or write to a removableoptical disk 522 (e.g., a CD-ROM or DVD). The HDD 614, FDD 516 andoptical disk drive 520 can be connected to the system bus 508 by a HDDinterface 524, an FDD interface 526 and an optical drive interface 528,respectively. The HDD interface 524 for external drive implementationscan include at least one or both of Universal Serial Bus (USB) and IEEE1394 interface technologies. Any of the described memory orcomputer-readable medium may implement the memory 269 of FIG. 2 or 414of FIG. 4. In addition, the number of data transformations, such as 242,244, 246, and 248 of FIG. 2 or 416 of FIG. 4 may also be stored in oneor more of the described memories or computer-readable media.

The drives and associated computer-readable media provide volatileand/or nonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For example, a number of program modules canbe stored in the drives and memory units 510, 512, including anoperating system 530, one or more application programs 532, otherprogram modules 534, and program data 536. In one embodiment, the one ormore application programs 532, other program modules 534, and programdata 536 can include, for example, the various applications and/orcomponents of a respective system, such as 100, 200 or apparatuses 241or 410.

A user can enter commands and information into the computer 502 throughone or more wire/wireless input devices, for example, a keyboard 538 anda pointing device, such as a mouse 540. Other input devices may includemicrophones, infra-red (IR) remote controls, radio-frequency (RF) remotecontrols, game pads, stylus pens, card readers, dongles, finger printreaders, gloves, graphics tablets, joysticks, keyboards, retina readers,touch screens (e.g., capacitive, resistive, etc.), trackballs, trackpads, sensors, styluses, and the like. These and other input devices areoften connected to the processing unit 504 through a communicationinterface 542 that is coupled to the system bus 508 but can be connectedby other interfaces such as a parallel port, IEEE 1394 serial port, agame port, a USB port, an IR interface, and so forth.

A monitor 544 or other type of display device is also connected to thesystem bus 508 via an interface, such as a video adaptor 546. Themonitor 544 may be internal or external to the computer 502.

The computer 502 may operate in a networked environment using logicalconnections via wire and/or wireless communications to one or moreremote computers, such as a remote computer 548. The remote computer 548can be a workstation, a server computer, a router, a personal computer,portable computer, microprocessor-based entertainment appliance, a peerdevice or other common network node, and typically includes many or allthe elements described relative to the computer 502, although, forpurposes of brevity, only a memory/storage device 550 is illustrated.The logical connections depicted include wire/wireless connectivity to alocal area network (LAN) 552 and/or larger networks, for example, a widearea network (WAN) 554. Such LAN and WAN networking environments arecommonplace in offices and companies, and facilitate enterprise-widecomputer networks, such as intranets, all of which may connect to aglobal communications network, for example, the Internet.

When used in a LAN networking environment, the computer 502 is connectedto the LAN 552 through a wire and/or wireless communication interface556. The communication interface 556 can facilitate wire and/or wirelesscommunications to the LAN 552, which may also include a wireless accesspoint disposed thereon for communicating with the wireless functionalityof the communication interface 556.

When used in a WAN networking environment, the computer 502 can includea modem 558, or is connected to a communications server on the WAN 554or has other means for establishing communications over the WAN 554,such as by way of the Internet. The modem 558, which can be internal orexternal and a wire and/or wireless device, connects to the system bus508 via the communication interface 556. In a networked environment,program modules depicted relative to the computer 502, or portionsthereof, can be stored in the remote memory/storage device 550. It willbe appreciated that the network connections shown are exemplary andother means of establishing a communications link between the computerscan be used.

The computer 502 is operable to communicate with wire and wirelessdevices or entities using the IEEE 802 family of standards, such aswireless devices operatively disposed in wireless communication (e.g.,IEEE 802.11 over-the-air modulation techniques). This includes at leastWi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wirelesstechnologies, among others. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices. Wi-Fi networks use radiotechnologies called IEEE 802.11(a, b, g, n, ac, etc.) to provide secure,reliable, fast wireless connectivity. A Wi-Fi network can be used toconnect computers to each other, to the Internet, and to wire networks(which may use IEEE 802.3-related media and functions).

The various elements of the devices as previously described withreference to FIGS. 1-5 may include various hardware elements, softwareelements, or a combination of both. Examples of hardware elements mayinclude devices, logic devices, components, processors, microprocessors,circuits, processors, circuit elements (e.g., transistors, resistors,capacitors, inductors, and so forth), integrated circuits, applicationspecific integrated circuits (ASIC), programmable logic devices (PLD),digital signal processors (DSP), field programmable gate array (FPGA),memory units, logic gates, registers, semiconductor device, chips,microchips, chip sets, and so forth. Examples of software elements mayinclude software components, programs, applications, computer programs,application programs, system programs, software development programs,machine programs, operating system software, middleware, firmware,software modules, routines, subroutines, functions, methods, procedures,software interfaces, application program interfaces (API), instructionsets, computing code, computer code, code segments, computer codesegments, words, values, symbols, or any combination thereof. However,determining whether an embodiment is implemented using hardware elementsand/or software elements may vary in accordance with any number offactors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output datarates, memory resources, data bus speeds and other design or performanceconstraints, as desired for a given implementation.

The components and features of the devices described above may beimplemented using any combination of discrete circuitry, applicationspecific integrated circuits (ASICs), logic gates and/or single chiparchitectures. Further, the features of the devices may be implementedusing microcontrollers, programmable logic arrays and/or microprocessorsor any combination of the foregoing where suitably appropriate. It isnoted that hardware, firmware and/or software elements may becollectively or individually referred to herein as “logic” or “circuit.”

FIG. 6 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as Naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of performing naturallanguage processing and understanding; scoring; bioinformatics;cheminformatics; software engineering; fraud detection; customersegmentation; generating online recommendations; adaptive websites; andthe like.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 6.

In block 604, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model. In embodiments, the training data may includetransaction information, historical transaction information, and/orinformation relating to transaction. The transaction information may befor a general population and/or specific to a user and user account in afinancial institutional database system.

In block 606, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model must find structurein the inputs on its own. In semi-supervised training, only some of theinputs in the training data are correlated to desired outputs.

In block 608, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, e.g., the current transactioninformation, the machine-learning model may have a high degree ofaccuracy. Otherwise, the machine-learning model may have a low degree ofaccuracy. The 90% number is an example only. A realistic and desirableaccuracy percentage is dependent on the problem and the data.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 606,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 610.

In block 610, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 612, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 614, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

It will be appreciated that the exemplary devices shown in the blockdiagrams described above may represent one functionally descriptiveexample of many potential implementations. Accordingly, division,omission or inclusion of block functions depicted in the accompanyingfigures does not infer that the hardware components, circuits, softwareand/or elements for implementing these functions would be necessarily bedivided, omitted, or included in embodiments.

At least one computer-readable storage medium may include instructionsthat, when executed, cause a system to perform any of thecomputer-implemented methods described herein.

Some embodiments may be described using the expressions “one example” or“an example” along with their derivatives. These terms mean that aparticular feature, structure, or characteristic described in connectionwith the example is included in at least one example. The appearances ofthe phrase “in one example” in various places in the specification arenot necessarily all referring to the same example. Moreover, unlessotherwise noted the features described above are recognized to be usabletogether in any combination. Thus, any features discussed separately maybe employed in combination with each other unless it is noted that thefeatures are incompatible with each other.

With general reference to notations and nomenclature used herein, thedetailed descriptions herein may be presented in terms of programprocedures executed on a computer or network of computers. Theseprocedural descriptions and representations are used by those skilled inthe art to most effectively convey the substance of their work to othersskilled in the art.

A procedure or function as referred to here, and generally, is conceivedto be a self-consistent sequence of operations leading to a desiredresult. These operations are those requiring physical manipulations ofphysical quantities. Usually, though not necessarily, these quantitiestake the form of electrical, magnetic or optical signals capable ofbeing stored, transferred, combined, compared, processed and otherwisemanipulated. It proves convenient at times, principally for reasons ofcommon usage, to refer to these signals as bits, values, elements,identifiers, types, symbols, characters, data, data points, information,terms, numbers, or the like. It should be noted, however, that all ofthese and similar terms are to be associated with the appropriatephysical quantities and are merely convenient labels applied to thosequantities.

Further, the manipulations performed are often referred to in terms,such as determining or comparing, which may be associated with mentaloperations performed by a human operator. No such capability of a humanoperator is necessary, or desirable in most cases, in any of theoperations described herein, which form part of one or more embodiments.Rather, the described operations are machine operations.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. These terms are notnecessarily intended as synonyms for each other. For example, someembodiments may be described using the terms “connected” and/or“coupled” to indicate that two or more elements are in direct physicalor electrical contact with each other. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other.

Various embodiments also relate to apparatus or systems for performingmachine learning based data transformation operations. The describedapparatus may be specially constructed for the required purpose and maybe selectively activated or reconfigured by a computer program stored inthe computer. The procedures presented herein are not inherently relatedto a particular computer or other apparatus. The required structure fora variety of these machines are apparent from the description given.

It is emphasized that the Abstract of the Disclosure is provided toallow a reader to quickly ascertain the nature of the technicaldisclosure. It is submitted with the understanding that it will not beused to interpret or limit the scope or meaning of the claims. Inaddition, in the foregoing Detailed Description, various features aregrouped together in a single embodiment to streamline the disclosure.This method of disclosure is not to be interpreted as reflecting anintention that the claimed embodiments require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment. In the appended claims, the terms“including” and “in which” are used as the plain-English equivalents ofthe respective terms “comprising” and “wherein,” respectively. Moreover,the terms “first,” “second,” “third,” and so forth, are used merely aslabels, and are not intended to impose numerical requirements on theirobjects.

What has been described above includes examples of a novel architecture.It is, of course, not possible to describe every conceivable combinationof components and/or methodologies, but one of ordinary skill in the artmay recognize that many further combinations and permutations arepossible. Accordingly, the novel architecture is intended to embrace allsuch alterations, modifications and variations that fall within thespirit and scope of the appended claims.

What is claimed is:
 1. An apparatus, comprising: a data structurecontaining data identifiers; a memory storing programming code and aplurality of data transformations that receive input from amachine-learning process; and processing circuitry, coupled to thememory, wherein the processing circuitry has an input and an output, andis operable to execute the stored programming code that causes theprocessing circuitry to perform functions, including functions to:receive first data from a first smart device; determine a data type ofthe received first data, wherein the determined data type is indicativeof a category of the first smart device; select, based on the determineddata type, a first data transformation from the plurality of datatransformations to be applied to the received first data; determine, forthe first data transformation, weighted inputs from a machine learningalgorithm trained using historical data of the determined data type;determine a correspondence between certain data elements in non-dialoginformation obtained from the received first data with a list ofhistorical data elements by using the determined weighted inputs in aprobability function applied to the obtained non-dialog information;transform the received first data into transformed data by applying thefirst data transformation to the certain data elements determined tohave correspondence; based on the transformed data, generate astandardized chain of values including individual relative probabilityvalues indicating a probability of certain data elements having thedetermined correspondence and respective relationship probability valuesindicating a probability of a relationship between respective dataelements of the certain data elements having the determinedcorrespondence; and outputting the standardized chain of values to achatbot training data structure, wherein the chatbot training datastructure is coupled via the output to the processing circuitry.
 2. Theapparatus of claim 1, wherein the memory further comprises: programmingcode stored in the memory that causes the processing circuitry toperform further functions, including functions to: receive second datafrom a second smart device; determine a data type of the received seconddata; based on the determined data type of the received second data,transform the received second data into transformed second data using asecond data transformation wherein the other data that receives inputfrom a machine learning algorithm, wherein the second datatransformation is the same or different from the first datatransformation; and outputting the transformed second data to thechatbot training data structure.
 3. The apparatus of claim 1, whereinthe determined data type is determined from multiple different datatypes based on a format of the received first data.
 4. The apparatus ofclaim 1, wherein the category of the first smart device as indicated bythe data type is one of a fitness device, a global positioning systemdevice, a smart phone application that provides non-dialog data, a homesecurity system, or an audio system.
 5. A system, comprising: a chatbotdata warehouse storing data including dialog data used to trainenterprise chatbots; a communication interface coupled to the chatbotdata warehouse and to an external network, wherein the communicationinterface receives, via the external network, anonymized non-dialog datafrom smart devices; a memory storing programming code; and a servercoupled to the memory and the communication interface, wherein theserver comprises processing circuitry that executes the programming codestored in the memory, which causes the server to perform functions,including functions to: obtain, via the communication interface, theanonymized non-dialog data; identify a data type of the anonymizednon-dialog data; based on the identified data type, select at least onemachine learning-based data transformation from a plurality of machinelearning-based data transformations; input the non-dialog data into eachmachine learning transformation selected from the plurality of machinelearning-based data transformations; for each respective machinelearning-based data transformation selected from the plurality ofmachine learning-based data transformations: parse the non-dialog datato obtain certain data elements specific to the identified data type;input the obtained certain data elements into each respective machinelearning-based data transformation; apply each respective machinelearning-based data transformation to the obtained certain dataelements, wherein input attributes to each respective machine-learningbased data transformation are weighted based on a machine learningalgorithm trained using previously obtained non-dialog data of a samedata type as the identified data type; and receive data transformed byeach respective machine learning-based data transformation; and storethe transformed data in the chatbot data warehouse.
 6. The system ofclaim 5, wherein the processing circuitry of the server upon furtherexecution of the programming code stored in the memory causes theprocessing circuit upon input of the non-dialog data into each of the atleast one selected machine learning-based data transformations toperform additional functions including functions to: generate datapoints and probability information related to one or more data pointsgenerated by each of the at least one selected machine learning-baseddata transformations; and output the data points and the probabilityinformation of each respective machine learning-based datatransformation of the at least one selected machine learning-based datatransformations.
 7. The system of claim 5, wherein the memory furthercomprises programming code stored in the memory, which causes the serverwhen applying each respective machine learning-based datatransformation, to perform additional functions, including functions to:determine, by applying a probability function to data elements in theobtained certain data elements, a correspondence between one or more ofthe certain data elements with a data element in a list of historicaldata elements; based on the determined correspondence, generate astandardized chain of values, wherein the standardized chain of valuesincludes relative and relationship probability values; and store thestandardized chain of values in the chatbot data warehouse.
 8. Thesystem of claim 7, wherein the memory further comprises programming codestored in the memory, which causes the server to perform additionalfunctions, including functions to: generate a context related vocabularyfor use by the enterprise chatbot based on the correspondence betweenthe one or more of the certain data elements with a data element in alist of historical data elements generated by the application of theprobability function to the data elements in the obtained certain dataelements.
 9. The system of claim 5, wherein a smart device of the smartdevices that provides anonymized, non-dialog data to the server is oneof: a wearable fitness smart device, a wearable smart device, anexercise device, a personal assistance device, an audio systemassistance device, or a home automation device.
 10. The system of claim5, wherein the server comprises processing circuitry that executesfurther programming code stored in the memory, which causes the serverto perform functions, including functions to: determining the identifieddata type from multiple different data types based on a format of thereceived anonymized, non-dialog data.
 11. The system of claim 5, whereinthe server comprises processing circuitry that executes furtherprogramming code stored in the memory, which causes the server toperform functions, including functions to: determine a category of asmart device as indicated by the identified data type is one of afitness device, a global positioning system device, a smart phoneapplication that provides non-dialog data, a home automation device, oran audio system.
 12. A non-transitory computer-readable storage mediumstoring computer-readable program code executable by a processor,wherein execution of the computer-readable program code causes theprocessor to: receive, via a coupling to an external network, a data setcontaining non-dialog data, wherein the data set is generated based ondata provided by a smart device; extract the non-dialog data from thereceived data set; determine a data type of the extracted non-dialogdata, wherein the determined data type is determined from multipledifferent data types based on a format of the received data set; select,based on the determined data type, a machine learning-based datatransformation to be applied to the extracted non-dialog data; apply theselected machine learning-based data transformation to the extractednon-dialog data to obtain standardized non-dialog data, wherein applyingthe selected machine learning-based data transformation to the extractednon-dialog data includes functions to: weight a data type attributebased on an output from a machine learning algorithm trained usingpreviously obtained non-dialog information of the same data type as thedetermined data type; input the weighted data type attribute into theselected machine learning-based data transformation; process theinputted weighted data type attributes according to the selected machinelearning-based data transformation; and output data points and, for eachdata point, data point probability information as a standardizednon-dialog data to a chatbot training data structure; and store thestandardized non-dialog data in the chatbot training data structure. 13.The non-transitory computer-readable storage medium of claim 12, whereinexecution of the computer-readable program code causes the processor tofurther: access a data structure containing data identifiers; comparethe extracted non-dialog data to data identifiers stored in the datastructure; based on a result of the comparison, identify the data typeof the extracted non-dialog data; and output the identified data type asthe determined data type.
 14. The non-transitory computer-readablestorage medium of claim 13, wherein the execution of thecomputer-readable program code causes the processor, when comparing theextracted non-dialog data to data identifiers stored in the datastructure, to further: determine whether the non-dialog data includes adata identifier corresponding to either a wearable fitness smart device,a wearable smart device, an exercise device, a personal assistancedevice, an audio system assistance device, or a home automation device;extract the data identifier; and match the extracted data identifier toat least one of the data identifiers stored in the data structurecontaining data identifiers; and output the matching extracted dataidentifier as the result of the comparison.
 15. The non-transitorycomputer-readable storage medium of claim 12, wherein the execution ofthe computer-readable program code causes the processor, when selectinga machine learning-based data transformation based on the determineddata type, to further: access a data structure containing a plurality ofmachine learning-based data transformations; and select the machinelearning-based data transformation from the plurality of machinelearning-based data transformations to be applied to the extractednon-dialog data.
 16. The non-transitory computer-readable storage mediumof claim 12, further comprising: programming code that causes theprocessor to perform further functions to process the inputted weighteddata type attribute according to the selected machine learning-baseddata transformation, including functions to: determine, by applying aprobability function including the weighted data type attribute to dataelements in the extracted non-dialog data, a correspondence betweencertain data elements in the extracted non-dialog data with a list ofhistorical data elements; based on the determined correspondence, applythe selected machine learning-based data transformation to the certaindata elements determined to have the correspondence in a historical dataelement in the list; and in response to applying the selected machinelearning-based data transformation, generate a standardized chain ofvalues as the data point probability information based on the determinedcorrespondence, wherein the standardized chain of values includesrelative probability values indicating a probability of a relationshipbetween respective data elements of the certain data elements having thedetermined correspondence.
 17. The non-transitory computer-readablestorage medium of claim 12, wherein execution of the computer-readableprogram code causes the processor to further: determine a category ofthe smart device as indicated by the data type is one of a fitnessdevice, a global positioning system device, a smart phone applicationthat provides non-dialog data, a home automation device, or an audiosystem.